Tool wear has been extensively studied by the machine tool industry and academia for over 50 years. In general, research has focused on correlating tool wear with machining signals, mainly cutting forces, tool vibration, and cutting temperature, to provide the necessary process information needed for the development of intelligent unmanned machining systems. Although these machining signals can be easily measured, an accurate correlation of machining signals to tool wear has not been possible.
During machining, the failure of a cutting tool is caused by wear due to the interactions between the tool and the workpiece (flank wear) and between the tool and the chip (crater wear). Guidelines and specifications for flank and crater wear measurement are available in machining handbooks. Traditionally, these small wear parameters are measured under laboratory conditions, using a toolmaker's microscope. However, these measurements provide a limited definition of the wear of a cutting edge. Tool wear is not simple in nature and because of the irregular boundaries and the varying surface textures, the flank and crater wear boundaries are difficult to define. As a result, measurements of the width or length of flank and crater wear contours are only approximations and are not repeatable because of measurement error. Moreover, it has been recognized by those skilled in the art that the area and eroded volume of a wear region are more relevant parameters for quantifying tool wear, but there has been no practical, accurate method for such measurements, particularly for tools installed in machines.
The U.S. Pat. No. 4,845,743 to Bandyopadhyay et al entitled "Tool Wear Measurements by Machine Vision" documents a response to that need by using machine vision to measure the area of tool wear. In that development image analysis is based on an interactive procedure using a general purpose image processing system. The present invention goes further by measuring wear volume as well as area, and accomplishes the measurement between machining processes in an unattended machining cell. The invention is not limited to tool measurements and has general application to the three dimensional measurement of object profiles using structured light patterns and image analysis algorithms. The application of a structured stripe pattern to extract three-dimensional information is well known. However, the success of the method hinges on tracking of the lines in the input image. In many cases, noise resulting from shadows, glares, surface texture, magnification, and light conditions make it too difficult to get a good image for accurate tracking.